Demircioğlu A
Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Hufelandstr. 55, 45147, Essen, Deutschland.
Pathologe. 2019 Dec;40(Suppl 3):271-276. doi: 10.1007/s00292-019-00704-8.
Radiomics deals with the statistical analysis of radiologic image data. In this article, radiomics is introduced and some of its applications are presented. In particular, an example is used to demonstrate that pathology and radiology can work together for better diagnoses. There is no denying that artificial intelligence will find its place in radiology (and pathology). Deep learning in particular will increasingly find applications. However, the impact on clinical routine is more long term and probably gradual, so AI will initially only be used in the form of specialized tools to support everyday clinical practice until methods and programs improve to the extent that AI can also take on more general diagnoses. However, this will not replace pathologists and radiologists in the long term, but rather turn them into "information specialists" who interpret the results obtained and integrate them into clinical contours.
放射组学涉及对放射影像数据的统计分析。本文介绍了放射组学，并展示了其一些应用。特别是，通过一个例子来说明病理学和放射学可以共同协作以实现更好的诊断。不可否认，人工智能将在放射学（以及病理学）领域占据一席之地。尤其是深度学习将越来越多地得到应用。然而，对临床常规的影响是更长期的，而且可能是渐进的，所以人工智能最初只会以专门工具的形式用于支持日常临床实践，直到方法和程序改进到人工智能也能承担更一般诊断的程度。然而，从长远来看，这并不会取代病理学家和放射科医生，而是会将他们转变为“信息专家”，来解读所获得的结果并将其整合到临床概况中。
